Part Of Adaptation Research Articles

A partial domain adaptation broad learning system for machinery fault diagnosis

A partial domain adaptation broad learning system for machinery fault diagnosis

Multi-source partial domain adaptation with Gaussian-based dual-level weighting for PPG-based heart rate estimation

Photoplethysmography (PPG) signals from wearable devices have expanded the accessibility of heart rate estimation. Recent advances in deep learning have significantly improved the generalizability of heart rate estimation from PPG signals. However, these models exhibit performance degradation when used for new subjects with different PPG distributions. Although previous studies have attempted subject-specific training and fine-tuning techniques, they require labeled data for each new subject, limiting their practicality. In response, we explore the application of domain adaptation techniques using only unlabeled PPG signals from the target subject. However, naive domain adaptation approaches do not adequately account for the variability in PPG signals among different subjects in the training dataset. Furthermore, they overlook the possibility that the heart rate range of the target subject may only partially overlap with that of the source subjects. To address these limitations, we propose a novel multi-source partial domain adaptation method, GAussian-based dUaL-level weighting (GAUL), designed for the PPG-based heart rate estimation, formulated as a regression task. GAUL considers and adjusts the contribution of relevant source data at the domain and sample levels during domain adaptation. The experimental results on three benchmark datasets demonstrate that our method outperforms existing domain adaptation approaches, enhancing the heart rate estimation accuracy for new subjects without requiring additional labeled data. The code is available at: https://github.com/Im-JihyunKim/GAUL.

Manifold Embedded Ensemble Partial Domain Adaptation: A Novel Partial-Set Transfer Mechanism for Gearboxes Fault Diagnosis

Manifold Embedded Ensemble Partial Domain Adaptation: A Novel Partial-Set Transfer Mechanism for Gearboxes Fault Diagnosis

A Low Computational Complexity Family of Spline NLMS Adaptive Filter Algorithm

This paper presents novel and low computational complexity spline adaptive filtering (SAF) algorithms based on partial coefficients selection and adaption. They are addressed partial update SAF normalized least mean squares (PU-SAF-NLMS) algorithms. The PU-SAF-NLMS equations are established via the steepest gradient descent constraint to the proposed cost function. In the proposed algorithms, the filter coefficients are updated based on periodic, sequential, and selective rules, leading to reduction in computational complexity. Furthermore, the convergence speed of PU-SAF-NLMS algorithms is close to the conventional SAF-NLMS. Moreover, the convergence analysis of algorithms is studied. Ultimately, the performance of the presented algorithms is appraised through several experimental results.

Multisource partial domain adaptation for bearing fault diagnosis

Abstract Domain adaptation has been widely used in fault diagnosis and dealt with data distribution discrepancies, but the labels in source and target domains are usually assumed to be identical. The complexity of the working conditions, in reality, leads to the fact that the labels in target domains are often a subset of the labels in source domains. This special case is called the partial domain problem. However, most of the existing proposed methods for solving partial domain problems are limited to single-source-domain scenarios and fail to effectively integrate multisource knowledge. Hence, this study proposes a new approach of multisource domain obfuscation-subdomain alignment (MSDO-SA) for partial domain adaptation fault diagnosis in multisource domains. Through domain obfuscation, the multisource domains are converted into a single source domain. The subdomain alignment aims at improving the generalization ability and relevance of the model, and effectively alleviates the domain shift problem. Finally, multiple partial domain fault diagnosis tasks using the CWRU dataset validate the effectiveness, robustness, and superiority of the proposed method.

Between Politics and Expert: Institutional Transposition and the Admission of Economic Bureaucrats into Party Elite in China

ABSTRACT Red and expert are two differing principles in organizing the political lives of the Chinese government. This study transcends conventional characterizations of the red-expert relationship as either one-sided domination or mere co-existence, exploring its hybrid manifestations and dissecting the nuanced intersections. To this end, this article analyzes the Chinese Communist Party’s recruitment strategy, particularly focusing on the assimilation of economic experts. Utilizing a dataset featuring high-ranking economic bureaucrats, the study reveals the intriguing mechanism of ‘institutional transposition’ used for reconciling institutional contradictions. This mechanism assesses economic policy professionals based on politically transposed criteria, leading to the promotion of economic bureaucrats with unique career attributes, notably specialists with broad organizational experiences, into key political leadership positions. The deployment of ‘generalist specialists’, this article proposes, bridges gaps within a fragmented bureaucracy and coordinates specialized ministries. This article exemplifies a partial adaptation of the Party and a pragmatic approach towards reconciling conflicting institutional logics.

Joint weight optimization for partial domain adaptation via kernel statistical distance estimation

The goal of Partial Domain Adaptation (PDA) is to transfer a neural network from a source domain (joint source distribution) to a distinct target domain (joint target distribution), where the source label space subsumes the target label space. To address the PDA problem, existing works have proposed to learn the marginal source weights to match the weighted marginal source distribution to the marginal target distribution. However, this is sub-optimal, since the neural network’s target performance is concerned with the joint distribution disparity, not the marginal distribution disparity. In this paper, we propose a Joint Weight Optimization (JWO) approach that optimizes the joint source weights to match the weighted joint source distribution to the joint target distribution in the neural network’s feature space. To measure the joint distribution disparity, we exploit two statistical distances: the distribution-difference-based L2-distance and the distribution-ratio-based χ2-divergence. Since these two distances are unknown in practice, we propose a Kernel Statistical Distance Estimation (KSDE) method to estimate them from the weighted source data and the target data. Our KSDE method explicitly expresses the two estimated statistical distances as functions of the joint source weights. Therefore, we can optimize the joint weights to minimize the estimated distance functions and reduce the joint distribution disparity. Finally, we achieve the PDA goal by training the neural network on the weighted source data. Experiments on several popular datasets are conducted to demonstrate the effectiveness of our approach. Intro video and Pytorch code are available at https://github.com/sentaochen/Joint-Weight-Optimation. Interested readers can also visit https://github.com/sentaochen for more source codes of the related domain adaptation, multi-source domain adaptation, and domain generalization approaches.

Retinomorphic Color Perception Based on Opponent Process Enabled by Perovskite Bipolar Photodetectors.

The ability to perceive color by the retina can be attributed to both its trichromatic photoreceptors and the antagonistic neural wiring known as the opponent process. While neuromorphic sensors have been shown to demonstrate memory and adaptation capabilities, color perception is still challenging due to the intrinsic lack of spectral selectivity in narrow bandgap semiconductors. Furthermore, research on emulating neural wiring is severely lacking. The combination of halide perovskite materials with a tunable bandgap and a novel bipolar photodetector design emulates the efficiency of the retina in processing color information. The stimuli-responsive material is also responsible for maintaining partial color constancy-an adaptation feature. Leveraging the unique enhancement of color contrasts, an in-sensor data compression and edge detection can also be demonstrated. The color perception, chromatic adaptation, and color contrast enhancement make perovskite bipolar photodetectors a unique example where the sensor and neural wiring can be co-developed in conjunction.

50: Partial adaptation for online-adaptive proton therapy triggered by prompt gamma imaging

50: Partial adaptation for online-adaptive proton therapy triggered by prompt gamma imaging

Attention guided partial domain adaptation for interpretable transfer diagnosis of rotating machinery

Domain adaptation (DA) is a well-established method to tackle the transfer diagnosis of rotating machinery. However, current DA methods encounter challenges in partial-set transfer diagnosis scenarios, where the target domain only possesses a subset of the source fault classes, due to several limitations: (1) assuming identical fault classes between the target and source domains, (2) neglecting the fine-grained differences among individual instances, and (3) lacking interpretability for transfer logic. To address these problems, this paper proposes an attention-guided partial domain adaptation (AGPDA) method for interpretable transfer diagnosis of rotating machinery. Firstly, to exclude outlier classes in the source domain, a transferability evaluator is presented to evaluate the potential benefits of source instances for domain alignment. Secondly, a novel cross-domain attention (CDA) mechanism is further developed, which leverages instance-wise attention to highlight relevant samples, enabling differentiated and effective knowledge transfer to various target samples. Finally, the CDA mechanism is implemented via a new interpretable linear cosine attention, which is specially designed to facilitate the exploration of the transfer logic. Comparison experiments demonstrate the superiority of AGPDA in both closed-set and partial-set transfer diagnosis. Furthermore, dual-perspective visualization of CDA reveals that target fault recognition primarily relies on source samples in the same class.

Gradient Alignment based Partial Domain Adaptation (GAPDA) using a domain knowledge filter for fault diagnosis of bearing

Fault diagnosis of rolling element bearings is essential to ensure the safety and reliability of industrial sites. However, changes in operating conditions can lead to variations in the distributions of the data that is collected for fault diagnosis. This, in turn, decreases the performance of deep-learning-based fault-diagnosis methods. In addition, most data in industrial settings are unlabeled, which leads to ineffectiveness of the supervised learning method. To address the issues of domain shift and unlabeled data, numerous studies have been conducted to reduce distribution discrepancies when using unlabeled data. Still, most of these studies assume that the number of labels in the training and test data are identical; this is not always true for data from industrial sites. Thus, the research outlined in this paper was pursued to address the partial domain adaptation problem, which occurs when there are fewer labels in the test data than in the training data. The proposed approach suggests two methods for applying partial domain adaptation in mechanical systems: i) a domain knowledge filter is proposed, which reflects fault characteristics in the original signal for effective feature extraction in the mechanical engineering domain, and ii) a gradient alignment module is defined to align the gradient of the statistical loss function. The method proposed herein was validated using two open-source datasets; the approach demonstrated high performance and low uncertainty, as compared to other prior methods. Additionally, physical analysis of the domain knowledge filter was conducted in this work.

A temporal partial domain adaptation network for transferable prognostics across working conditions with insufficient data

Variations in equipment working conditions cause distribution drifts in condition monitoring data, thus impeding the accuracy of remaining useful life (RUL) prediction. Although numerous transfer learning methods have been developed for cross-domain prognostics, it is still challenging to collect enough target domain data to meet training demands because of economic and safety concerns. Therefore, a temporal partial domain adaptation network (TPDAN) is proposed for transferable prognostics across working conditions without run-to-failure data in the target domain. Firstly, the domain discrepancy is appropriately measured by a stage-weighted maximum mean discrepancy (SW-MMD), allowing for the inconsistency of the RUL distribution of available data between domains. Secondly, the ranking-based feature regularization is developed to align the relative position of samples in the feature space and the label space. It facilitates the exploitation of temporal information inherent in the samples and restrains the destruction of the degradation properties of features. By performing domain adaptation based on the above tools, domain-invariant and degradation-aware features can be extracted by the TPDAN to provide reliable knowledge transfer across working conditions. The effectiveness of TPDAN has been validated, and experimental results demonstrate the compelling transferable RUL prediction performance even when run-to-failure data is unavailable in the target domain.

Global variation in the preferred temperature for recreational outdoor activity

The impacts of climate change will depend on how human societies adapt to higher temperatures. In this study we report empirical evidence suggesting that people living in warmer places prefer a higher temperature for their recreational outdoor activities. To arrive at this conclusion we examine a novel global dataset of mobile phone usage in parks in more than 2500 locations across 77 countries. We examine this dataset with econometric methods to identify the relationship between outdoor recreation and temperature from daily variation in weather. Overall we find that for every increase in annual mean temperature by 1 degree Celsius, the preferred daily mean temperature for outdoor activity increases by about 0.5 degrees Celsius. We explain how these results can be interpreted as evidence for partial adaptation. We also illustrate how ignoring adaptation can bias projections of future responses to climate change.

The partial adaptation strategy for online-adaptive proton therapy: A proof of concept study in head and neck cancer patients.

The accuracy of intensity-modulated proton therapy (IMPT) is greatly affected by anatomy variations that might occur during the treatment course. Online plan adaptations have been proposed as a solution to intervene promptly during a treatment session once the anatomy changes are detected. The implementation of online-adaptive proton therapy (OAPT) is still hindered by time-consuming tasks in the workflow. The study introduces the novel concept of partial adaptation and aims at investigating its feasibility as a potential solution to parallelize tasks during an OAPT workflow for saving valuable in-room time. The proof-of-principle simulation study includes datasets from six head and neck cancer (HNC) patients, each consisting of one planning CT (pCT) and three contoured control CTs (cCTs). Robust 3-field normo-fractionated initial IMPT plans were generated on the pCTs with a standardized field configuration, delivering 66 Gy and 54Gy to the high-risk and low-risk clinical target volume (CTVHigh and CTVLow), respectively.For each cCT, a dose-mimicking-based partial adaptation was applied: two fields were adapted on the current anatomy taking into account the background dose of the first non-adapted field supposedly delivered in the meantime. Fraction doses on the cCTs resulting from partially adapted plans with different first (non-adapted) field assignments were compared against those from non-adapted and fully adapted plans regarding target coverage and organs at risk (OARs) sparing. The robustness of partially adapted plans was also evaluated. Partially adapted plans showed comparable results to fully adapted plans and were superior to non-adapted plans for both target coverage and OAR sparing. Target coverage degradation in the non-adapted plans (median D98%: 95.9% and 97.5% for CTVLow and CTVHigh, respectively) was recovered by both partial (98.0% and 98.5%) and full adaptation (98.2% and 98.7%) in comparison to the initial plans (98.7% and 98.8%). The initial hotspot dose for the CTVHigh (median D2%: 101.8%) increased in the non-adapted plans (102.9%) and was recovered by the adaptive strategies (partial: 102.5%, full: 101.9%). The near-maximum dose (D0.01cc) to brainstem and spinal cord was within clinical constraints for all investigated dose distributions, but clearly increased for no adaptation and improved in the (both partially and fully) adapted plans with respect to the non-adapted ones. The parotids' median doses (D50) were mainly patient-specific depending on the proximity to the target region, but anyway lower for the partially and fully adapted plans compared to the non-adapted ones. OAR sparing was furthermore improved for the partially adapted plans in comparison to full adaptation. Robustness of the target dose metrics was preserved in all evaluated scenarios. For OAPT of HNC patients, partial adaptation is able to generate plans of superior conformity to non-adapted plans and of comparable conformity as fully adapted plans, while having the potential to speed up the online-adaptive workflows. Thus, partial adaptation represents an intermediate approach until fast online adaptation workflows become available. Furthermore, it can be applied in workflows where online treatment verification stops the delivery and triggers an online adaptation for the remaining fraction.

Partial Domain Adaptation in Remaining Useful Life Prediction With Incomplete Target Data

Partial Domain Adaptation in Remaining Useful Life Prediction With Incomplete Target Data

Maximum likelihood weight estimation for partial domain adaptation

Partial Domain Adaptation (PDA) aims to generalize a classification model from a labeled source domain to an unlabeled target domain, where the source label space contains the target label space. There are two main challenges in PDA that weaken the model's classification performance in the target domain: (i) the joint distribution of the source domain is related but different from that of the target domain, and (ii) the source outlier data, whose labels do not belong to the target label space, have a negative impact on learning the target classification model. To tackle these challenges, we propose a Maximum Likelihood Weight Estimation (MLWE) approach to estimate a weight function for the source domain. The weight function matches the joint source distribution of the relevant part to the joint target distribution, and reduces the negative impact of the source outlier data. To be specific, our approach estimates the weight function by maximizing a likelihood function, and the estimation leads to a nice convex optimization problem that has a global optimal solution. In the experiments, our approach demonstrates superior performance on popular benchmark datasets. Intro video and PyTorch code are available at https://github.com/sentaochen/Maximum-Likelihood-Weight-Estimation.

PECULIARITIES OF ADAPTATION AND COURSE OF POST-TRAUMATIC STRESS DISORDER IN SERVICEMEN DEPLOYED TO INTENSE COMBAT ZONES

In the context of war, post-traumatic stress disorder (PTSD) emerges as one of the most prevalent psychological conditions, particularly among military personnel deployed in combat zones. The primary triggers for PTSD in military personnel often include the loss of comrades or the necessity to use lethal weapons, resulting in significant consequences. For PTSD to manifest, individuals must undergo a traumatic experience that exceeds normal human stress levels and leads to distress. The aim of this study is to investigate the course of PTSD in military personnel who have been exposed to intense combat operations and to explore their maladaptive manifestations. The study comprised 38 patients diagnosed with PTSD (F43.1) according to the ICD-10, who received treatment at the Regional Mental Health Care Institution for the Protection of the Armed Forces. All patients provided informed consent to participate in the study. A structured clinical diagnostic interview known as CAPS-DX was applied in this study. Developed based on the DSM-IV, this interview assesses the symptoms of posttraumatic stress disorder (PTSD), the duration of the disorder, and the degree of impairment of social functioning. The CAPS-DX scale provided insights into the presence of PTSD symptoms both currently and throughout the individual's life following psychotrauma. To diagnose psychosocial maladjustment and its characteristics, we employed the Psychosocial Maladjustment Scale by L. Herasymenko (2018). Clinical and anamnestic examinations revealed that 86% of servicemen had experienced multiple traumatic events, indicating a history of several psychotraumas. The study identified that the primary psychopathological manifestations of PTSD did not differ significantly from the traditional course of the disorder, but they were more intense and pronounced in this group of servicemen. The study has demonstrated that servicemen with combat experience and PTSD exhibited reduced levels of psychosocial adaptation. Their adaptive resources and capabilities, as well as their neuropsychological stability, were significantly diminished, along with a decrease in their communication abilities. Based on the overall level and quality of social and psychological adaptation, three levels of adaptation/maladaptation were distinguished among servicemen with combat-related PTSD: sufficient, partial, or maladaptation. It should be noted that the levels of adaptation were correlated with the severity of anxiety. Servicemen with sufficient adaptation typically exhibited low levels of anxiety, while those with partial adaptation showed an average level, and those with maladaptation displayed high levels of anxiety. The study revealed a spectrum of psychopathological manifestations in servicemen who were in the combat zone, ranging from semiotic complexes resembling panic attacks to psychogenic catalepsy. Furthermore, it was found that servicemen with combat experience and post-traumatic stress disorder had reduced levels of psychosocial adaptation, which correlated with the severity of anxiety.

An auto-regulated universal domain adaptation network for uncertain diagnostic scenarios of rotating machinery

In recent years, domain adaptation techniques have garnered significant attention in the field of intelligent fault diagnosis for mechanical equipment. Domain adaptation techniques can effectively enable the reuse of diagnostic knowledge, thereby improving the generalization performance of the trained model. However, many intelligent diagnostic models are tailored to specific diagnostic scenarios, including closed-set domain adaptation, partial domain adaptation, and open-set domain adaptation. When these models built for specific scenarios are applied to other scenarios, their diagnostic performance will deteriorate catastrophically. This study proposes an auto-regulated universal domain adaptation network (AUDAN) for uncertain diagnostic scenarios of rotating machinery, which can handle multiple diagnostic scenarios and adaptively set threshold boundaries for each fault category based on data distribution structure to distinguish known and unknown samples. Specifically, a novel integration loss function is designed to train the proposed AUDAN. The self-adaptive reweight module is introduced into the proposed method to distribute the relative weight for each loss function automatically. Experiments on two rotating machinery datasets are carried out for validations. The experimental results demonstrate that the proposed AUDAN performs effectively in uncertain diagnostic scenarios and is promising in addressing the fault classification tasks in real industries.

A dual-weighted adversarial network for partial domain fault diagnosis of machinery

Domain adaptation provides a promising approach to cross-domain fault diagnosis of rotating machinery. While many current methods focus on scenarios where the source and target domains share identical label spaces, a prevalent situation in industrial production involves the target domain being a subset of the source domain, known as partial domain adaptation (PDA). The main challenge in PDA is the label mismatches caused by outlier classes, making the alignment between domains particularly difficult. To this end, a dual-weighted adversarial network is proposed in this paper. Specifically, a bilateral class weighting strategy (BCWS) is developed, which can effectively suppress negative migration at the decision boundary while improving the robustness of adversarial training by applying bilateral weighting to both the source and target domains. Moreover, a collaborative framework is developed to facilitate positive migration. The constructed global perceptual module (GPM) is highly correlated with the improved adversarial loss function, which adaptively adjusts the input feature map according to the accuracy of the target domain classification and pays more attention to the domain-invariant features. Experimental results for two cases, namely across different operating conditions and across different rotating components, verify the effectiveness and superiority of the proposed method for the PDA problem.

Small is Beautiful: Compressing Deep Neural Networks for Partial Domain Adaptation.

Domain adaptation is a promising way to ease the costly data labeling process in the era of deep learning (DL). A practical situation is partial domain adaptation (PDA), where the label space of the target domain is a subset of that in the source domain. Although existing methods yield appealing performance in PDA tasks, it is highly presumable that computation overhead exists in deep PDA models since the target is only a subtask of the original problem. In this work, PDA and model compression are seamlessly integrated into a unified training process. The cross-domain distribution divergence is reduced by minimizing a soft-weighted maximum mean discrepancy (SWMMD), which is differentiable and functions as regularization during network training. We use gradient statistics to compress the overparameterized model to identify and prune redundant channels based on the corresponding scaling factors in batch normalization (BN) layers. The experimental results demonstrate that our method can achieve comparable classification performance to state-of-the-art methods on various PDA tasks, with a significant reduction in model size and computation overhead.